DBMS > Amazon Redshift vs. EXASOL vs. HEAVY.AI vs. Hive

System Properties Comparison Amazon Redshift vs. EXASOL vs. HEAVY.AI vs. Hive

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Editorial information provided by DB-Engines

Name

Description

Large scale data warehouse service for use with business intelligence tools

High-performance, in-memory, MPP database specifically designed for in-memory analytics.

A high performance, column-oriented RDBMS, specifically developed to harness the massive parallelism of modern CPU and GPU hardware

data warehouse software for querying and managing large distributed datasets, built on Hadoop

Primary database model

Relational DBMS

Secondary database models

Spatial DBMS

DB-Engines Ranking measures the popularity of database management systems
Trend Chart

Score	17.94
Rank	#34	Overall
	#21	Relational DBMS

Score	1.99
Rank	#124	Overall
	#58	Relational DBMS

Score	1.77
Rank	#141	Overall
	#65	Relational DBMS

Score	61.17
Rank	#18	Overall
	#12	Relational DBMS

Website

aws.amazon.com/redshift

www.exasol.com

github.com/heavyai/heavydb
www.heavy.ai

hive.apache.org

Technical documentation

docs.aws.amazon.com/redshift

www.exasol.com/resources

docs.heavy.ai

cwiki.apache.org/confluence/display/Hive/Home

Developer

Amazon (based on PostgreSQL)

Exasol

HEAVY.AI, Inc.

Apache Software Foundation

initially developed by Facebook

Initial release

2012

2000

2016

2012

Current release

5.10, January 2022

3.1.3, April 2022

License

Commercial or Open Source

commercial

Open Source

Apache Version 2; enterprise edition available

Open Source

Apache Version 2

Cloud-based only

Only available as a cloud service

yes

DBaaS offerings (sponsored links)

Database as a Service

Providers of DBaaS offerings, please contact us to be listed.

Implementation language

C++ and CUDA

Java

Server operating systems

hosted

Linux

All OS with a Java VM

Data scheme

yes

Typing

predefined data types such as float or date

yes

XML support

Some form of processing data in XML format, e.g. support for XML data structures, and/or support for XPath, XQuery or XSLT.

Secondary indexes

restricted

yes

SQL

Support of SQL

yes

does not fully support an SQL-standard

yes

SQL-like DML and DDL statements

APIs and other access methods

JDBC
ODBC

.Net
JDBC
ODBC
WebSocket

JDBC
ODBC
Thrift
Vega

JDBC
ODBC
Thrift

Supported programming languages

All languages supporting JDBC/ODBC

Java
Lua
Python
R

All languages supporting JDBC/ODBC/Thrift
Python

C++
Java
PHP
Python

Server-side scripts

Stored procedures

user defined functions

in Python

user defined functions

yes

user defined functions and integration of map-reduce

Triggers

yes

Partitioning methods

Methods for storing different data on different nodes

Sharding

Round robin

Sharding

Replication methods

Methods for redundantly storing data on multiple nodes

yes

Multi-source replication

selectable replication factor

MapReduce

Offers an API for user-defined Map/Reduce methods

yes

Hadoop integration

yes

query execution via MapReduce

Consistency concepts

Methods to ensure consistency in a distributed system

Immediate Consistency

Eventual Consistency

Foreign keys

Referential integrity

yes

informational only, not enforced by the system

yes

Transaction concepts

Support to ensure data integrity after non-atomic manipulations of data

ACID

Concurrency

Support for concurrent manipulation of data

yes

Durability

Support for making data persistent

yes

In-memory capabilities

Is there an option to define some or all structures to be held in-memory only.

yes

User concepts

Access control

fine grained access rights according to SQL-standard

Access rights for users, groups and roles according to SQL-standard

fine grained access rights according to SQL-standard

Access rights for users, groups and roles

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